Towards the optimisation of ceramic-based microbial fuel cells: A three-factor three-level response surface analysis design

Abstract

Highlights Ceramic-based MFCs fed with urine for continuous bioenergy production. Response surface methodology for designing a sequential set of MFC experiments. Obtaining a second order model with a regression coefficient of 0.886. High effect of the external loading and anode area on the MFC power output. Membrane thickness does not show statistical influence on the MFC performance.

A soil microbial fuel cell-based biosensor for dissolved oxygen monitoring in water

“…The model fits the variability of the data well, with a strong positive R 2 adj of 0.92. The model is also meaningful, as expressed by the high F-value of 37.5 (p< 0.001), indicating a linear correlation between the sensor response and the explanatory variables [15]. The predictive power of the model is also high, as expressed by the R 2 pred in Table 2.…”

“…The calibration models were evaluated based on the ANOVA analysis in terms of the adjusted R 2 (R 2 adj), which measures the variability of the data explained by the model; the F-value, which indicates how significant the model is; the predicted R 2 (R 2 pred) that estimates the predictive power of the model; and the significance of the coefficients, which are indicated by the p-value [14,15].…”

Marketability Prospects of Microbial Fuel Cells for Sustainable Energy Generation

“…Regardless of the previous developments, the prerequisites using less expensive materials have further halted the utilization of this innovation on the industrial scale . This has led to the extensive use of porous ceramic membranes (PCMs) as separators in MFCs. − The PCMs have numerous merits, such as cost-effectiveness and mechanical, chemical, and thermal stability, that make them a perfect alternative for the polymer ion-exchange membranes. Rajesh et al reported that PCMs play a significant role in enhancing the MFC stability, which can enhance the columbic efficiency and give a better environment to the microbes.…”

A robust correlation based on dimensional analysis to characterize microbial fuel cells

“…However given the large number of parameters and their complex interdependence, it is difficult to obtain a direct correlation with a set of important parameters. Some studies in the past have provided scaling relationships or correlations between individual parameters or with a small subset [16][17][18][19][20][21] . While such expressions are useful, they are often limited to the particular experimental setup and operating conditions, as they are focused on just 1 or 2 parameters and ignore the interdependencies with others.…”